Abstract

We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked content into the indexes for Google, Bing, Yahoo!, and DuckDuckGo, which renders information entirely different from the keywords used to locate it, enabling spam, profane, or possibly illegal content to go unnoticed by these search engines but still returned in unrelated search results. Furthermore, we provide compelling demonstrations of the content subversion attack's efficacy on widely employed QR codes and one-dimensional barcodes. Finally, considering the prevalent avoidance of optical character recognition (OCR) due to computational overhead, we propose a comprehensive and lightweight alternative mitigation method.

Department(s)

Computer Science

Publication Status

Early Access

Keywords and Phrases

Barcode; Content Subversion; Malicious Font; PDF; QR code

International Standard Serial Number (ISSN)

1941-0018; 1545-5971

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2025

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